Search Results for "wsl"

...Whether searching by tags or entering free-text queries, NPDB executes SQL queries efficiently, providing accurate results in no time. Developed primarily in C++, NPDB ensures seamless operation on Linux, Windows (via WSL), and Chrome OS.

...The GUI was designed to provide a stunning experience powered by state-of-the-art technologies without feeling clunky and outdated like other alternatives. It features blazing-fast TensorRT inference by NVIDIA, which can speed up AI processes significantly. Pre-packaged, without the need to install Docker or WSL (Windows Subsystem for Linux) - and NCNN inference by Tencent which is lightweight and runs on NVIDIA, AMD and even Apple Silicon - in contrast to the mammoth of an inference PyTorch is, which only runs on NVIDIA GPUs.

ping command implementation in Go but with colorful output and pingu ascii art. Colorful and fun output. Cross-platform support: Windows, macOS and Linux (also WSL) It works with a single executable file, so it can be installed easily. Supports IPv4 and IPv6. Simply specify the target host name or IP address in the first argument e.g. pingu github.com or pingu 13.114.40.48. You can change the number of transmissions by specifying the -c option. You can download executable binaries from the latest release page.

irs is a command-line application that downloads audio and metadata in order to package an mp3 with both. Extensible, the user can download individual songs, entire albums, or playlists from Spotify. Note that the binaries right now have only been tested on WSL. They should run on most Linux distros, and OS X, but if they don't please make an issue. At it's core irs downloads individual songs. It does this by interfacing with the Spotify API, grabbing metadata, and then searching Youtube for a video containing the song's audio. It will download the video using youtube-dl, extract the audio using ffmpeg, and then pack the audio and metadata together into an MP3. ...

Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test...